Diabolo Orientation Stabilization by Learning Predictive Model for Unstable Unknown-Dynamics Juggling Manipulation
Takayuki Murooka, Kei Okada, Masayuki Inaba
Abstract
Juggling manipulation is one of difficult manipulation to acquire since some of such manipulation is unstable and also its physical model is unknown due to the complex non-prehensile manipulation. To acquire these unstable unknown-dynamics juggling manipulation, we propose a method for designing the predictive model of manipulation with a deep neural network, and a real-time optimal control law with some robustness and adaptability using backpropagation of the network. In this study, we apply this method to diabolo orientation stabilization, which is one of unstable unknown-dynamics juggling manipulation. We verify the effectiveness of the proposed method by comparing with basic controllers such as P Controller or PID Controller, and also check the adaptability of the proposed controller by some experiments with a real life-sized humanoid robot.
BibTeX
@inproceedings{iros2020_diaboloorientati,
title = {Diabolo Orientation Stabilization by Learning Predictive Model for Unstable Unknown-Dynamics Juggling Manipulation},
author = {Takayuki Murooka and Kei Okada and Masayuki Inaba},
booktitle = {IROS 2020},
year = {2020}
}